Approximate Quantum Random Access Memory Architectures

• URL: http://arxiv.org/abs/2210.14804v2
• Date: Thu, 27 Oct 2022 05:06:06 GMT
• Title: Approximate Quantum Random Access Memory Architectures
• Authors: Koustubh Phalak, Junde Li and Swaroop Ghosh
• Abstract summary: Quantum supremacy in many applications using well-known quantum algorithms rely on availability of data in quantum format. We propose an approximate Parametric Quantum Circuit (PQC) based QRAM which takes address lines as input and gives out the corresponding data in these address lines as the output. We present two applications of the proposed PQC-based QRAM namely, storage of binary data and storage of machine learning (ML) dataset for classification.
• Score: 7.509129971169722
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Quantum supremacy in many applications using well-known quantum algorithms rely on availability of data in quantum format. Quantum Random Access Memory (QRAM), an equivalent of classical Random Access Memory (RAM), fulfills this requirement. However, the existing QRAM proposals either require qutrit technology and/or incur access challenges. We propose an approximate Parametric Quantum Circuit (PQC) based QRAM which takes address lines as input and gives out the corresponding data in these address lines as the output. We present two applications of the proposed PQC-based QRAM namely, storage of binary data and storage of machine learning (ML) dataset for classification.

Related papers

• QuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum Circuits [82.50620782471485]
  QuantumSEA is an in-time sparse exploration for noise-adaptive quantum circuits. It aims to achieve two key objectives: (1) implicit circuits capacity during training and (2) noise robustness. Our method establishes state-of-the-art results with only half the number of quantum gates and 2x time saving of circuit executions.
  arXiv  Detail & Related papers  (2024-01-10T22:33:00Z)
• Systems Architecture for Quantum Random Access Memory [0.6386668251980657]
  Quantum random access memory (QRAM) is a promising architecture for realizing quantum queries. We show how to leverage the intrinsic biased-noise resilience of the proposed QRAM for implementation on either Noisy Intermediate-Scale Quantum (NISQ) or Fault-Tolerant Quantum Computing (FTQC) hardware.
  arXiv  Detail & Related papers  (2023-06-05T20:52:28Z)
• QRAM: A Survey and Critique [1.52292571922932]
  Quantum random-access memory (QRAM) is a mechanism to access data based on addresses which are themselves a quantum state. We use two primary categories of QRAM from the literature: active and passive. In summary, we conclude that cheap, scalableally passive QRAM is unlikely with existing proposals.
  arXiv  Detail & Related papers  (2023-05-17T15:48:48Z)
• Quantum Random Access Memory For Dummies [4.608607664709314]
  Quantum Random Access Memory (QRAM) has the potential to revolutionize the area of quantum computing. QRAM uses quantum computing principles to store and modify quantum or classical data efficiently.
  arXiv  Detail & Related papers  (2023-05-02T03:24:16Z)
• Delegated variational quantum algorithms based on quantum homomorphic encryption [69.50567607858659]
  Variational quantum algorithms (VQAs) are one of the most promising candidates for achieving quantum advantages on quantum devices. The private data of clients may be leaked to quantum servers in such a quantum cloud model. A novel quantum homomorphic encryption (QHE) scheme is constructed for quantum servers to calculate encrypted data.
  arXiv  Detail & Related papers  (2023-01-25T07:00:13Z)
• Resource-efficient simulation of noisy quantum circuits and application to network-enabled QRAM optimization [0.7107001348724662]
  We introduce a resource-efficient method for simulating large-scale noisy entanglement. We analyze Chen et al.'s network-based QRAM as an application at the scale of quantum data centers or near-term quantum internet.
  arXiv  Detail & Related papers  (2022-10-24T18:00:05Z)
• Iterative Qubits Management for Quantum Index Searching in a Hybrid System [56.39703478198019]
  IQuCS aims at index searching and counting in a quantum-classical hybrid system. We implement IQuCS with Qiskit and conduct intensive experiments. Results demonstrate that it reduces qubits consumption by up to 66.2%.
  arXiv  Detail & Related papers  (2022-09-22T21:54:28Z)
• Quantum Federated Learning with Quantum Data [87.49715898878858]
  Quantum machine learning (QML) has emerged as a promising field that leans on the developments in quantum computing to explore large complex machine learning problems. This paper proposes the first fully quantum federated learning framework that can operate over quantum data and, thus, share the learning of quantum circuit parameters in a decentralized manner.
  arXiv  Detail & Related papers  (2021-05-30T12:19:27Z)
• Quantum walk processes in quantum devices [55.41644538483948]
  We study how to represent quantum walk on a graph as a quantum circuit. Our approach paves way for the efficient implementation of quantum walks algorithms on quantum computers.
  arXiv  Detail & Related papers  (2020-12-28T18:04:16Z)
• Quantum random access memory via quantum walk [0.0]
  A novel concept of quantum random access memory (qRAM) employing a quantum walk is provided. Our scheme is fully parallelized. Consequently, only O(n) steps are required to access and retrieve O(2n) data in the form of quantum superposition states.
  arXiv  Detail & Related papers  (2020-08-31T04:54:12Z)
• Parallelising the Queries in Bucket Brigade Quantum RAM [69.43216268165402]
  Quantum algorithms often use quantum RAMs (QRAM) for accessing information stored in a database-like manner. We show a systematic method to significantly reduce the effective query time by using Clifford+T gate parallelism. We conclude that, in theory, fault-tolerant bucket brigade quantum RAM queries can be performed approximately with the speed of classical RAM.
  arXiv  Detail & Related papers  (2020-02-21T14:50:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.